Design Space Optimization Research Articles

Design and nonlinear modeling of a novel planar compliant parallelogram mechanism with general tensural-compresural beams

The paper proposes a novel type of Compliant Mechanism (CM) called Planar Compliant Parallelogram Mechanism (PCPM) for translational movement by synthesizing 8 compliant beams. Half of the beams are arranged in an inverted way while the other half of them are non-inverted, and both the inverted beams and non-inverted beams are arranged symmetrically. The arrangement empowers the mechanism to have a tensural-compresural characteristic with the robustness against beam buckling regardless of the high-payload direction. Moreover, the new mechanism introduces more design parameters than the traditional parallelogram mechanism, providing a broader design space for performance optimization. A nonlinear and analytical model of the mechanism was derived first depending on the Timoshenko Beam Constraint Model (TBCM) for quick design, followed by nonlinear FEA validation. Constraints and other conditions were determined beforehand to prepare for the comprehensive parameter analysis. Comprehensive analysis of geometric parameters was carried out for four single design objectives based on the aforementioned analytical model. It is shown that two types of optimized design were obtained from the parameter analysis where one design involves distributed compliance. Experimental testing was conducted to validate the primary motion stiffness of the distributed-compliance involved design.

Autonomous Electrolyte Discovery for Batteries with Experimentally Informed Bayesian Optimization

An autonomous battery electrolyte experimental platform capable of mixing multi-component electrolyte systems and characterizing the transport and electrochemical properties in a high-throughput manner is disclosed. A Bayesian optimization software package found novel electrolyte compositions through optimization of the high-dimensional electrolyte design space over key design objectives like electrochemical stability and conductivity.Electrolyte optimization is difficult because 1) electrolyte evaluation is expensive and takes time, and 2) the space of possible electrolytes is expansive, formed by many possible choices for solvents (often in ternary blends), salts (often in binary mixtures), and trace additives. Bayesian optimization methods are well suited for the optimization of high-dimensional functions with costly evaluations, often producing an efficient design-of-experiments to converge on multi-objective optimal formulations in few experiments. To expedite the optimization over the expansive design space, theoretical predictions of electrolyte properties via the Advanced Electrolyte Model were utilized as “priors” in the statistical model.Implementation of the novel experimental platform was carried out by two novel test stands developed to automate the mixing and characterization of electrolytes: Otto (for aqueous systems) and Clio (for aprotic/organic systems). The test stands characterized the ionic conductivity and electrochemical stability of electrolyte systems, featuring a four-electrode conductivity probe, pH meter, and a flow-through three-electrode cell and potentiostat. Clio also integrated electrochemically active electrodes for optimization of electrolyte/electrode systems. The active electrode systems used were common functional ceramic metal oxides.A software orchestration and data layer linked the test-stands to human experimenters and machine-learning packages through a web-services architecture; all experiment data and meta-data is saved in a database. Additional out-of-the-loop characterization was conducted on cathodic systems to validate composition, structure, and oxidation state.The aqueous design space consisted of aqueous blends of lithium and sodium salts, including nitrates, sulfates, and other commonly-used battery salts. High-concentration aqueous electrolyte candidates were discovered by optimizing of electrochemical voltage stability and conductivity, including low-cost, high-performing alternatives to known but costly aqueous electrolytes (e.g. LiTFSI). Clio’s design space includes blends of both aprotic organic solvents and solutes in additional to various compositions of electrochemically active electrodes. The test-stands are demonstrated to be significantly faster than common human experimentation techniques, converging on novel, optimized electrolyte mixtures in mere hours or days of experimentation.

Optimal process design space to ensure maximum viability and productivity in Penicillium chrysogenum pellets during fed-batch cultivations through morphological and physiological control

BackgroundBiomass growth of Pencillium chrysogenum is characterised by a distinct pellet morphology consisting of compact hyphal agglomerates. Fungal pellets are advantageous in industrial process control due to rheological advantages but lead to biomass degradation due to diffusional limitations of oxygen and substrate in the pellet’s core. Several fermentation parameters are known to affect key pellet characteristics regarding morphology, viability and productivity. Pellet morphology and size are affected by agitation. Biomass viability and productivity are tightly interlinked with substrate uptake and dissolved oxygen concentration.ResultsThe goal of this study was to study the impact of the fermentation parameters power input, dissolved oxygen content and specific substrate uptake rate on morphology, biomass viability and productivity. A design of experiments (DoE) approach was conducted and corresponding responses were analysed using novel morphological descriptors analysed by a previously established flow cytometry method. Results clearly display inverse correlations between power input and pellet size, specific morphological parameters related to pellet density can be increased in direct proportion to power input. Biomass viability and productivity are negatively affected by high specific substrate uptake rates.ConclusionsBased upon multiple linear regression, it was possible to obtain an optimal design space for enhanced viability and productivity at beneficial morphological conditions. We could maintain a high number of pellets with favourable morphology at a power input of 1500 W/m3. A sound compromise between viability and high productivity is possible at a specific glucose uptake rate of 0.043 g/g/h at dissolved oxygen levels of 40% minimum.

Heat Transfer Prediction for Methane in Regenerative Cooling Channels with Neural Networks

Methane is considered a good choice as a propellant for future reusable launch systems. However, the heat transfer prediction for supercritical methane flowing in the cooling channels of a regeneratively cooled combustion chamber is challenging. Because accurate heat transfer predictions are essential to design reliable and efficient cooling systems, heat transfer modeling is a fundamental issue to address. Advanced computational fluid dynamics (CFD) calculations achieve sufficient accuracy, but the associated computational cost prevents an efficient integration in optimization loops. Surrogate models based on artificial neural networks (ANNs) offer a great speed advantage. It is shown that an ANN, trained on data extracted from samples of CFD simulations, is able to predict the maximum wall temperature along straight rocket engine cooling channels using methane with convincing precision. The combination of the ANN model with simple relations for pressure drop and enthalpy rise results in a complete reduced-order model, which can be used for numerically efficient design space exploration and optimization.

The Correlation and Balance of Material Properties for DC Cable Insulation at Design Field

The innovation of materials with disruptive properties can be efficiently guided by improved physical understanding of material design principles. The design of a polymeric insulation depends on the desired requirements of the specific application, which, in the case of DC cable insulation, can be stated in terms of the following properties: controlled electrical conductivity, low space charge accumulation and high breakdown strength. Full characterization and detailed understanding of these properties as well as their correlation and balance may bring the ability to engineer needed dielectric properties for using as DC cable insulation. The aim of this paper is to identify the optimal DC insulation design space and to develop a formalism of the correlation between the conductivity and space charge, guided by a relatively simple model based on two physical parameters, activation energy ($\xi$ ) and mean trap separation ($\lambda$ ). With respect to implications for practical material design, the study demonstrates that a polymer material with activation energies in the range of 0.4 to 0.5 eV with relatively high trap density ($N$ ) ($N= \lambda ^{-3}$ , $\lambda =1$ nm, $N=1\text {E}+27\,\,\text {m}^{-3}$ ) can be suitable for HVDC cable insulation.

A Reconfigurable Design for Omni-adaptive Grasp Learning

The engineering design of robotic grippers presents an ample design space for optimization towards robust grasping. In this paper, we adopt the reconfigurable design of the robotic gripper using a novel soft finger structure with omni-directional adaptation, which generates a large number of possible gripper configurations by rearranging these fingers. Such reconfigurable design with these omni-adaptive fingers enables us to systematically investigate the optimal arrangement of the fingers towards robust grasping. Furthermore, we adopt a learning-based method as the baseline to benchmark the effectiveness of each design configuration. As a result, we found that a 3-finger and 4-finger radial configuration is the most effective one achieving an average 96\% grasp success rate on seen and novel objects selected from the YCB dataset. We also discussed the influence of the frictional surface on the finger to improve the grasp robustness.

空间遥感相机大口径反射镜结构优化设计

In order to satisfy the stringent requirement for high surface shape accuracy and thermal stability of large-aperture mirrors in the complex space environment, a lightweight design for a Φ660 mm-diameter mirror was carried out. A method for creating the initial structure of the mirror using the classical theoretical formula, combining sensitivity analysis and parameter optimization for comprehensive design was proposed. Firstly, the parametric model was established, the influence law of the structure parameters of the mirror on the surface shape change was studied, and then iterations for the structural parameters with high sensitivity to the mirror surface RMS value were optimized through sensitivity analysis. Compared with the traditional mirror design model, this method reduced the optimization design space, saved computational cost and time, could globally optimize in the design space, and converged quickly to the optimal value. The mass of optimized mirror was 13.6 kg and the lightweight rate of the mirror reached 78.4%. The PV value of mirror surface accuracy was less than λ/10 and RMS value was less λ/40(λ=632.8 nm) under gravity load. The first-order frequency 121 Hz of the mirror assembly met the dynamic stiffness requirements of the mirror. Finally, based on the optimized results, the optimal mirror was put into production.

Study on Interior Space Optimization of Green Affordable Housing in Cold Region

With the construction of a large number of affordable housing and the development of green buildings, the design of green affordable housing has become a new concern and research topic in China’s residential architectural design, and the interior design of affordable housing has become a top priority. This article takes Changchun city as an example, through the research of affordable housing in Changchun, visit, found the main problems existing in the status quo, in view of the problems put forward housing interior space design optimization principle, and according to the design principles of indoor space scale, provides a good green indoor living environment for green residential tenants in cold areas.

Towards Ultra Low Cobalt Cathodes: A High Fidelity Computational Phase Search of Layered Li-Ni-Mn-Co Oxides

Layered Li(Ni,Mn,Co,)O2 (NMC) presents an intriguing ternary alloy design space for optimization as a cathode material in Li-ion batteries. In the case of NMC, however, only a select few proportions of transition metal cations have been attempted and even fewer have been adopted on a large scale. Recently, the high cost and resource limitations of Co have added a new design constraint and high Ni-containing NMC alloys have gained enormous attention despite possible performance trade-offs. Although the limited collection of NMC cathodes have been successful in providing the performance needed for many applications, specifically electric vehicles, this concern around Co requires further advancement and optimization within the NMC design space. Additionally, it is not fully understood if this material space is a disordered solid solution at room temperature and any arbitrary combination can be used or if there exist distinct transition metal orderings to which meta-stable solid solutions will decay during cycling and affect performance. Here, we present a high fidelity computational search of the ternary phase diagram with an emphasis on high-Ni, and thus low Co, containing compositional phases to understand the room temperature stability of the ordered and disordered solid solution phases. This is done through the use of density functional theory training data fed into a reduced order model Hamiltonian that accounts for effective electronic and spin interactions of neighboring transition metal atoms at various lengths in a background of fixed composition and position lithium and oxygen atoms. This model can then be solved to include finite temperature thermodynamics into a convex hull analysis to understand the regions of ordered and disordered solid solution as well the transition metal orderings within the ordered region of the phase diagram. We also provide a method to propagate the uncertainty at every level of the analysis to the final prediction of thermodynamically favorable compositional phases thus providing a quantitative measure of confidence for each prediction made. Due to the complexity of the three component system, as well as the intrinsic error of density functional theory, we argue that this propagation of uncertainty, particularly the uncertainty due to exchange-correlation functional choice is necessary to have reliable and interpretable results. We find that for the majority of transition metal compositions of the layered material, specifically medium to high-Ni content, prefer transition metal ordering and predict the collection of preferred compositions in the ordered region.

A two-stage hybrid optimization for honeycomb-type cellular structures under out-of-plane dynamic impact

Honeycomb structures with better balance between lightweight and crashworthiness have aroused growing attentions. However, structural parameters design by traditional optimization algorithm in small design space is not sufficient to significantly enhance the specific energy absorption (SEA) with the lower peak acceleration (amax). In this paper, a two-stage hybrid optimization for honeycomb-type cellular parameters is proposed to achieve rapid positioning of design space and significantly increase crashworthiness in a larger variable domain under out-of-plane dynamic impact. In stage I, a Taguchi-based grey correlation discrete optimization, combining Taguchi analysis, grey relational analysis, analysis of variance (ANOVA) with grey entropy measurement, is performed to determine the initial optimal value with a higher robustness and the significant influence variables. In stage II, a multi-objective design technique, namely non-nominated sorting genetic algorithm II based on surrogated model, is adopted to maximize the SEA and minimize the amax in a relatively small design domain. And it is found that the proposed two-stage hybrid method can broaden the optimal design space compared to that of traditional method attributable to its center point positioned by stage I. And the final optimization based on the proposed strategy is superior to the original structure, i.e., the SEA is increased by 47.55% and the amax is decreased by 80.8%. Therefore, the proposed algorithm can also be used to solve other more complicated engineering problems in a large design space with insightful design data.

A parallel multi-fidelity optimization approach in induction hardening

Purpose Reliable modeling of induction hardening requires a multi-physical approach, which makes it time-consuming. In designing an induction hardening system, combining such model with an optimization technique allows managing a high number of design variables. However, this could lead to a tremendous overall computational cost. This paper aims to reduce the computational time of an optimal design problem by making use of multi-fidelity modeling and parallel computing. Design/methodology/approach In the multi-fidelity framework, the “high-fidelity” model couples the electromagnetic, thermal and metallurgical fields. It predicts the phase transformations during both the heating and cooling stages. The “low-fidelity” model is instead limited to the heating step. Its inaccuracy is counterbalanced by its cheapness, which makes it suitable for exploring the design space in optimization. Then, the use of co-Kriging allows merging information from different fidelity models and predicting good design candidates. Field evaluations of both models occur in parallel. Findings In the design of an induction heating system, the synergy between the “high-fidelity” and “low-fidelity” model, together with use of surrogates and parallel computing could reduce up to one order of magnitude the overall computational cost. Practical implications On one hand, multi-physical modeling of induction hardening implies a better understanding of the process, resulting in further potential process improvements. On the other hand, the optimization technique could be applied to many other computationally intensive real-life problems. Originality/value This paper highlights how parallel multi-fidelity optimization could be used in designing an induction hardening system.

The use of hybrid intuitive class shape transformation curves in aerodynamic design

The inherent mathematical smoothness of intuitive class shape transformation (iCST) curves has been shown to be suitable for the design of aerodynamic shapes. However, this property means that any changes to a constraint are not local but will result in a modification to the whole curve. This poses a problem to the aerodynamic designer when different parts of the curve are required to fulfil particular design requirements. A Hybrid iCST (HiCST) parameterisation approach is proposed which allows two sections of a single aero-line curve to be decoupled, without geometric discontinuity, whilst maintaining the dimensionality of a design problem. The HiCST approach has been tested on two key aerodynamic components of an aero-engine. Firstly, a design space exploration and optimisation were carried out for an aero-engine fan cowl. A comparison of Pareto fronts showed a 3.9% reduction in the minimum achievable nacelle drag from the iCST to the HiCST parameterisation. Secondly, aero-engine intakes were designed with both the iCST and HiCST parameterisations. The HiCST intake showed improved aerodynamic performance in terms of DC60 and IPR and proved more insensitive to changes in massflow and incidence. This development of the method for an aero-engine fan cowl and intake highlights the potential aerodynamic benefit from the proposed HiCST method.

A geometry optimization framework for transcatheter heart valve leaflet design

Transcatheter aortic valve replacement (TAVR) is an established treatment for patients with severe symptomatic aortic stenosis. It is known and recognized that leaflet geometry has a key role in structural and hemodynamic performance of bioprosthetic heart valves. Excessive mechanical stress on the leaflets will lead to accelerated tissue degeneration and diminished long-term valve durability. The goal of this study was to develop an automatic optimization framework by means of commercially available software packages to reduce maximum stress value of transcatheter aortic valve (TAV) leaflets. Leaflet design was parameterized by 2 s-order non-uniform rational B-splines (NURBS) curves and particle swarm optimization method was used to examine the optimization design space. Optimized leaflet geometry for 23-mm and 26-mm TAVs were obtained under dynamic physiological loading condition. Leaflet stress distributions of the optimized TAV geometries were compared with two commercially available bioprostheses (i) Carpentier-Edwards PERIMOUNT Magna surgical bioprosthesis and (ii) Edwards SAPIEN 3 transcatheter heart valve. A considerable reduction in the maximum in-plane principal stress was observed in the optimized TAV geometries compared to the commercially available bioprostheses. The optimization results underline the opportunity to improve leaflet design in the next generation of TAVs to potentially increase long-term durability of transcatheter heart valves.

Sensitivity Analysis and Technology Evaluation for a Roadable Personal Air Vehicle at the Conceptual Design Stage

In this study, the technology identification, evaluation, and selection (TIES) method was implemented to explore an optimum design space appropriate for a personal air vehicle (PAV) at the conceptual design stage. A morphological matrix was employed to identify possible alternative configurations and performance targets. The Microsoft Excel add-in JMP, a commercial statistical tool, and a PAV sizing tool developed for this study were used for modelling and simulation. After the screening test, seven design variables having significant impacts on the design were finally chosen, specifically the range, maximum speed, cruise speed, cruise altitude, passengers, takeoff ground roll, and stall speed. Response surface equations (RSEs) were created as a function of the seven design variables. The generated RSEs were then used to perform a Monte Carlo simulation (MCS) to explore a feasible design space. As a result, it was confirmed that all seven design variables can be employed for an optimization process. In addition, k-factor and technology sensitivity analyses were conducted to evaluate applicable technologies quantitatively. Consequently, the selected set includes a flow circulation flap, leading edge blowing, a nanocoating, liquid metal, and an advanced composite material, which are technologies that greatly influenced the target criteria. Furthermore, the target value variations were analyzed as the k factors changed.

Study on famous prescription of prestigious traditional Chinese medicine master Lu Zhizheng based on QbD concept: optimization of extraction process and granule forming process of Meiyan Shuqi Prescription

Based on the concept of quality by design( QbD),the design space of Meiyan Shuqi Prescription extraction process was established,and its granules forming process was optimized. The content of total flavonoids was the critical quality attribute( CQA).The amount of water,extraction time and frequency were used as the critical process parameters( CPPs). Box-Behnken experimental design was used to optimize the extraction process. The design space of Meiyan Shuqi Prescription extraction process was established and verified. The results of variance analysis showed that the P value of the quadratic regression model was less than 0. 01,which indicated that the model had a statistical significance,and the relationship between each factor and response value could be functionalized by the established model. Through further optimization of design space,the optimal extraction process of Meiyan Shuqi prescription was13-fold volume of water,extraction for 3 times and each extraction time of 1. 4 hours. Furthermore,the forming process optimized by single factor was as follows: the excipient was dextrin; the ratio of extract powder to excipient was 1 ∶1; the wetting agent was 70% ethanol; and the amount of wetting agent was 0. 3 m L·g-1. The Meiyan Shuqi Prescription extraction process and the forming process based on QbD concept provide reference for the study of other traditional Chinese medicine prescriptions.

Design for Crashworthiness of Categorical Multimaterial Structures Using Cluster Analysis and Bayesian Optimization

Abstract This work introduces a cluster-based structural optimization (CBSO) method for the design of categorical multimaterial structures subjected to crushing, dynamic loading. The proposed method consists of three steps: conceptual design generation, design clustering, and Bayesian optimization. In the first step, a conceptual design is generated using the hybrid cellular automaton (HCA) algorithm. In the second step, threshold-based cluster analysis yields a lower-dimensional design. Here, a cluster validity index for structural optimization is introduced in order to qualitatively evaluate the clustered design. In the third step, the optimal design is obtained through Bayesian optimization, minimizing a constrained expected improvement function. This function allows to impose soft constraints by properly redefining the expected improvement based on the maximum constraint violation. The Bayesian optimization algorithm implemented in this work has the ability to search over (i) a real design space for sizing optimization, (ii) a categorical design space for material selection, or (iii) a mixed design space for concurrent sizing optimization and material selection. With the proposed method, materials are optimally selected based on multiple attributes and multiple objectives without the need for material ranking. The effectiveness of this approach is demonstrated with the design for crashworthiness of multimaterial plates and thin-walled structures.

HEIF: Highly Efficient Stochastic Computing-Based Inference Framework for Deep Neural Networks

Deep convolutional neural networks (DCNNs) are one of the most promising deep learning techniques and have been recognized as the dominant approach for almost all recognition and detection tasks. The computation of DCNNs is memory intensive due to large feature maps and neuron connections, and the performance highly depends on the capability of hardware resources. With the recent trend of wearable devices and Internet of Things, it becomes desirable to integrate the DCNNs onto embedded and portable devices that require low power and energy consumptions and small hardware footprints. Recently stochastic computing (SC)-DCNN demonstrated that SC as a low-cost substitute to binary-based computing radically simplifies the hardware implementation of arithmetic units and has the potential to satisfy the stringent power requirements in embedded devices. In SC, many arithmetic operations that are resource-consuming in binary designs can be implemented with very simple hardware logic, alleviating the extensive computational complexity. It offers a colossal design space for integration and optimization due to its reduced area and soft error resiliency. In this paper, we present HEIF, a highly efficient SC-based inference framework of the large-scale DCNNs, with broad applications including (but not limited to) LeNet-5 and AlexNet , that achieves high energy efficiency and low area/hardware cost. Compared to SC-DCNN, HEIF features: 1) the first (to the best of our knowledge) SC-based rectified linear unit activation function to catch up with the recent advances in software models and mitigate degradation in application-level accuracy; 2) the redesigned approximate parallel counter and optimized stochastic multiplication using transmission gates and inverse mirror adders; and 3) the new optimization of weight storage using clustering. Most importantly, to achieve maximum energy efficiency while maintaining acceptable accuracy, HEIF considers holistic optimizations on cascade connection of function blocks in DCNN, pipelining technique, and bit-stream length reduction. Experimental results show that in large-scale applications HEIF outperforms previous SC-DCNN by the throughput of $4.1\times $ , by area efficiency of up to $6.5\times $ , and achieves up to ${5.6\times }$ energy improvement.

Identifying a Loss-in-Weight Feeder Design Space Based on Performance and Material Properties

Powder properties, such as density and adhesion, can cause large variability in the flow rate of ingredients fed from feeders, which can propagate through the system. Knowing an ideal range of operation and correlating powder properties to process performance can result in faster optimization of performance, which saves time and money. A K-Tron KT-20 pharmaceutical loss-in-weight feeder was evaluated with multiple screw types, feed rates, and materials. The material was dispensed onto a catch scale, recording mass versus time, and analyzed for relative standard deviation and deviation from the setpoint. The materials used in this work were characterized by particle size, packing, and flow properties. Linear relationships between the feeder’s throughput capacity and material bulk density were identified for four different types of screws. Analyzing the feeder data resulted in a region where the RSD was at a minimum across a range of screw speeds. This was identified between 20–90% screw speed for coarse concave, fine concave, and coarse auger screws and 40–90% screw speed for fine auger screws. An optimal range of operation for K-Tron KT20 feeders was identified, as well as a linear correlation between material conditioned bulk density and maximum throughput for a given material and screw type. Using both the predictable maximum throughput and the optimal screw range, an optimal design space has been identified that will save development time and money.

A set strategy approach for multidisciplinary robust design optimization under interval uncertainty

Uncertainties widely exist in complex engineering systems. Robust design is one of the most used method for designing under uncertainty and has been gaining more attention. For the wide range of uncertainties, this article proposes a multidisciplinary robust design optimization method based on the set strategy. In this method, a robust design model that utilizes the maximum variation analysis is developed for uncertainty analysis. Then, a set strategy–based approach is employed to build a system optimization model, which is used to coordinate the coupling variables between full autonomy subsystems and obtains a new design space. Finally, the system robust optimal solution and the optimal robust design space are obtained through the sequential optimization, which provide a direction for the subsequent analysis. Two mathematics examples and the speed reducer design problem are taken to verify the validity and accuracy of the proposed method. A practical engineering problem, namely, air cooling battery thermal management system design problem, is successfully solved by the proposed method.

Efficient learning in metabolic pathway designs through optimal assembling

Engineering biology is a key enabling technology at the forefront of the new industrial bioeconomy. Rapid prototyping for bio-based production of chemicals and materials in the new biofoundries faces the challenge of dealing with increasingly complex libraries of genetic circuits consisting of multiple gene variants from different sources and with different translational tuning, along with multiple promoter libraries, different vector copy number, resistance cassette, or host strain. In order to streamline the biomanufacturing pipeline, smart design rules are necessary to find the trade-offs between experimental design and predictive strain modeling for synthetic biology production of chemicals. Here, we explore the Pareto surface spanned by the optimal experimental design space of combinatorial libraries that are found in a large-scale diverse set of genetic circuits and plasmid vectors, and learning efficiency of their associated metabolic pathway dynamics. Engineering rules for metabolic pathway design are validated by these means, suggesting optimal synthetic biology design approaches for biomanufacturing pipelines.